Step 9 of 15
Chapter 4: Machine Learning (Part 1)
The Three Learning Paradigms · Nearest Neighbors and Recommendation Systems.
Nearest Neighbors and Recommendation Systems
We delve into distance metrics, teaching learners how algorithms calculate Euclidean distance in multi-dimensional space to find data points that share similarities. This mechanism drives the k-Nearest Neighbors (k-NN) algorithm and powers modern content recommendation engines via Collaborative Filtering.
When streaming platforms or shopping sites recommend products, they identify clusters of users with similar past behaviors and cross-recommend their preferences. We examine the social risks of this technology, particularly how algorithmic curation can trap users in isolated filter bubbles and echo chambers. In regional village gatherings (Mel or Samaj), agricultural knowledge spreads through human collaborative filtering: if two farmers share similar soil conditions and water cistern capacity (Kund), the successful seed selection (Binj) of one farmer is naturally recommended to the other.
Regression and Predictive Trend Lines
When the target outcome is a continuous numerical value rather than a discrete category, we use Regression Analysis. We begin with Linear Regression, showing how algorithms fit a straight line through scatter plots by optimizing weight coefficients and baseline intercepts (using the method of least squares). Learners practice predicting continuous outcomes, such as estimating seasonal water retention in a Palar-Johad based on historical rainfall millimeters.
We then transition to Logistic Regression, which uses a S-shaped sigmoid curve to squash linear combinations into probabilities between 0 and 1. This technique is invaluable for predicting binary outcomes, such as calculating the probability that a student will pass a course based on hours studied, or estimating the likelihood that a drought-resistant Sevan grass crop will thrive under specific soil moisture levels.